Description

The widespread adoption of computer vision models is often constrained by the issue of domain mismatch. Models that are trained with data belonging to one distribution, perform poorly when tested

The widespread adoption of computer vision models is often constrained by the issue of domain mismatch. Models that are trained with data belonging to one distribution, perform poorly when tested with data from a different distribution. Variations in vision based data can be attributed to the following reasons, viz., differences in image quality (resolution, brightness, occlusion and color), changes in camera perspective, dissimilar backgrounds and an inherent diversity of the samples themselves.

Reuse Permissions
  • 2.87 MB application/pdf

    Download count: 0

    Details

    Contributors
    Date Created
    • 2017
    Resource Type
  • Text
  • Collections this item is in
    Note
    • Doctoral Dissertation Computer Science 2017

    Machine-readable links